def testCountLocalDateMetrics(self):
met_result = metrics.summarize_by_local_date(self.testUUID,
ecwl.LocalDate({'year': 2015, 'month': 8}),
ecwl.LocalDate({'year': 2015, 'month': 9}),
'MONTHLY', ['count'], True)
self.assertEqual(list(met_result.keys()), ['aggregate_metrics', 'user_metrics'])
user_met_result = met_result['user_metrics'][0]
agg_met_result = met_result['aggregate_metrics'][0]
logging.debug(met_result)
# local timezone means that we only have one entry
self.assertEqual(len(user_met_result), 1)
self.assertEqual(user_met_result[0].nUsers, 1)
self.assertEqual(user_met_result[0].ON_FOOT, 6)
self.assertEqual(user_met_result[0].BICYCLING, 4)
self.assertEqual(user_met_result[0].IN_VEHICLE, 5)
# We are not going to make assertions about the aggregate values since
# they are affected by other entries in the database but we expect them
# to be at least as much as the user values
self.assertEqual(len(agg_met_result), 1)
self.assertEqual(agg_met_result[0].nUsers, 2)
self.assertGreaterEqual(agg_met_result[0].BICYCLING,
user_met_result[0].BICYCLING + 1) # 21s has one bike trip
self.assertGreaterEqual(agg_met_result[0].ON_FOOT,
user_met_result[0].ON_FOOT + 3) # 21s has three bike trips
self.assertGreaterEqual(agg_met_result[0].IN_VEHICLE,
user_met_result[0].IN_VEHICLE + 3) # 21s has three motorized trips
def testCountLocalDateMetrics(self):
met_result = metrics.summarize_by_local_date(
self.testUUID, ecwl.LocalDate({
'year': 2015,
'month': 8
}), ecwl.LocalDate({
'year': 2015,
'month': 9
}), 'MONTHLY', ['count'], True)
self.assertEqual(list(met_result.keys()),
['aggregate_metrics', 'user_metrics'])
user_met_result = met_result['user_metrics'][0]
agg_met_result = met_result['aggregate_metrics'][0]
logging.debug(met_result)
# local timezone means that we only have one entry
self.assertEqual(len(user_met_result), 1)
self.assertEqual(user_met_result[0].nUsers, 1)
self.assertEqual(user_met_result[0].WALKING, 12)
self.assertNotIn('BICYCLING', user_met_result[0])
self.assertEqual(user_met_result[0].BUS, 4)
# We are not going to make assertions about the aggregate values since
# they are affected by other entries in the database but we expect them
# to be at least as much as the user values
self.assertEqual(len(agg_met_result), 1)
self.assertEqual(agg_met_result[0].nUsers, 2)
self.assertGreaterEqual(agg_met_result[0].WALKING,
user_met_result[0].WALKING +
5) # 21s has three bike trips
self.assertGreaterEqual(agg_met_result[0].BUS, user_met_result[0].BUS +
2) # 21s has three motorized trips
def is_matched_user(user_id, spec):
metric_list = get_metric_list(spec["checks"])
time_type = spec['time_type']
if 'from_local_date' in spec and 'to_local_date' in spec:
freq_metrics = enam.summarize_by_local_date(user_id,
spec["from_local_date"],
spec["to_local_date"],
spec["freq"],
metric_list,
include_aggregate=False)
elif 'start_time' in spec and 'end_time' in spec:
freq_metrics = enam.summarize_by_timestamp(user_id,
spec["start_time"],
spec["end_time"],
spec["freq"],
metric_list,
include_aggregate=False)
else:
# If no start and end times are specified, we assume that this is a
# timestamp query because we can come up with a reasonable start and end
# time for timestamps but not for local_dates, which are basically a filter.
# so if we run this on the first of a month, for example, we won't find
# anything, which seems bogus and not what people would expect
assert time_type == "timestamp", "time_type = %s, expected timestamp" % time_type
freq_metrics = enam.summarize_by_timestamp(user_id,
0,
time.time(),
spec["freq"],
metric_list,
include_aggregate=False)
assert (freq_metrics is not None)
assert ('user_metrics' in freq_metrics)
curr_user_metrics = freq_metrics['user_metrics']
checks = spec['checks']
check_results = np.zeros(len(checks))
for i, check in enumerate(checks):
curr_metric_result = curr_user_metrics[i]
# curr_freq_result is a list of ModeStatTimeSummary objects, one for each
# grouped time interval in the range
# e.g. for daily, 2017-01-19, 2017-01-20, 2017-01-21, 2017-01-22, 2017-01-23, ....
for msts in curr_metric_result:
# We defined our check as being true if it is true for _any_ grouped time
# period in the range. So as long as we find a match for that check, we are
# good!
if matches_check(check, msts):
check_results[i] = True
logging.info("For user_id %s, check result array = %s, all? %s" %
(user_id, check_results, np.all(check_results)))
return np.all(check_results)
def testCountNoEntries(self):
# Ensure that we don't crash if we don't find any entries
# Should return empty array instead
# Unlike in https://amplab.cs.berkeley.edu/jenkins/job/e-mission-server-prb/591/
met_result_ld = metrics.summarize_by_local_date(self.testUUID,
ecwl.LocalDate({'year': 2000}),
ecwl.LocalDate({'year': 2001}),
'MONTHLY', ['count'], True)
self.assertEqual(list(met_result_ld.keys()), ['aggregate_metrics', 'user_metrics'])
self.assertEqual(met_result_ld['aggregate_metrics'][0], [])
self.assertEqual(met_result_ld['user_metrics'][0], [])
met_result_ts = metrics.summarize_by_timestamp(self.testUUID,
arrow.get(2000,1,1).timestamp,
arrow.get(2001,1,1).timestamp,
'm', ['count'], True)
self.assertEqual(list(met_result_ts.keys()), ['aggregate_metrics', 'user_metrics'])
self.assertEqual(met_result_ts['aggregate_metrics'][0], [])
self.assertEqual(met_result_ts['user_metrics'][0], [])
def testCountNoEntries(self):
# Ensure that we don't crash if we don't find any entries
# Should return empty array instead
# Unlike in https://amplab.cs.berkeley.edu/jenkins/job/e-mission-server-prb/591/
met_result_ld = metrics.summarize_by_local_date(self.testUUID,
ecwl.LocalDate({'year': 2000}),
ecwl.LocalDate({'year': 2001}),
'MONTHLY', ['count'], True)
self.assertEqual(met_result_ld.keys(), ['aggregate_metrics', 'user_metrics'])
self.assertEqual(met_result_ld['aggregate_metrics'][0], [])
self.assertEqual(met_result_ld['user_metrics'][0], [])
met_result_ts = metrics.summarize_by_timestamp(self.testUUID,
arrow.get(2000,1,1).timestamp,
arrow.get(2001,1,1).timestamp,
'm', ['count'], True)
self.assertEqual(met_result_ts.keys(), ['aggregate_metrics', 'user_metrics'])
self.assertEqual(met_result_ts['aggregate_metrics'][0], [])
self.assertEqual(met_result_ts['user_metrics'][0], [])
def is_matched_user(user_id, spec):
metric_list = get_metric_list(spec["checks"])
time_type = spec['time_type']
if 'from_local_date' in spec and 'to_local_date' in spec:
freq_metrics = enam.summarize_by_local_date(user_id,
spec["from_local_date"], spec["to_local_date"],
spec["freq"], metric_list, include_aggregate=False)
elif 'start_time' in spec and 'end_time' in spec:
freq_metrics = enam.summarize_by_timestamp(user_id,
spec["start_time"], spec["end_time"],
spec["freq"], metric_list, include_aggregate=False)
else:
# If no start and end times are specified, we assume that this is a
# timestamp query because we can come up with a reasonable start and end
# time for timestamps but not for local_dates, which are basically a filter.
# so if we run this on the first of a month, for example, we won't find
# anything, which seems bogus and not what people would expect
assert time_type == "timestamp", "time_type = %s, expected timestamp" % time_type
freq_metrics = enam.summarize_by_timestamp(user_id,
0, time.time(), spec["freq"], metric_list, include_aggregate=False)
assert(freq_metrics is not None)
assert('user_metrics' in freq_metrics)
curr_user_metrics = freq_metrics['user_metrics']
checks = spec['checks']
check_results = np.zeros(len(checks))
for i, check in enumerate(checks):
curr_metric_result = curr_user_metrics[i]
# curr_freq_result is a list of ModeStatTimeSummary objects, one for each
# grouped time interval in the range
# e.g. for daily, 2017-01-19, 2017-01-20, 2017-01-21, 2017-01-22, 2017-01-23, ....
for msts in curr_metric_result:
# We defined our check as being true if it is true for _any_ grouped time
# period in the range. So as long as we find a match for that check, we are
# good!
if matches_check(check, msts):
check_results[i] = True
logging.info("For user_id %s, check result array = %s, all? %s" % (user_id, check_results, np.all(check_results)))
return np.all(check_results)
def testCountLocalDateMetrics(self):
met_result = metrics.summarize_by_local_date(
self.testUUID, ecwl.LocalDate({
'year': 2015,
'month': 8
}), ecwl.LocalDate({
'year': 2015,
'month': 9
}), 'MONTHLY', 'count')
self.assertEqual(met_result.keys(),
['aggregate_metrics', 'user_metrics'])
user_met_result = met_result['user_metrics']
agg_met_result = met_result['aggregate_metrics']
logging.debug(met_result)
# local timezone means that we only have one entry
self.assertEqual(len(user_met_result), 1)
self.assertEqual(user_met_result[0].nUsers, 1)
self.assertEqual(user_met_result[0].ON_FOOT, 6)
self.assertEqual(user_met_result[0].BICYCLING, 4)
self.assertEqual(user_met_result[0].IN_VEHICLE, 5)
# We are not going to make assertions about the aggregate values since
# they are affected by other entries in the database but we expect them
# to be at least as much as the user values
self.assertEqual(len(agg_met_result), 1)
self.assertEqual(agg_met_result[0].nUsers, 2)
self.assertGreaterEqual(agg_met_result[0].BICYCLING,
user_met_result[0].BICYCLING +
1) # 21s has one bike trip
self.assertGreaterEqual(agg_met_result[0].ON_FOOT,
user_met_result[0].ON_FOOT +
3) # 21s has one bike trip
self.assertGreaterEqual(agg_met_result[0].IN_VEHICLE,
user_met_result[0].IN_VEHICLE +
3) # 21s has one bike trip
